Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

目的：探讨穿心莲内酯（Andro）调节脂肪酸合成酶（Fas）/脂肪酸合成酶配体（FasL）信号轴对子宫内膜癌Ishikawa细胞顺铂（DDP）耐药性的影响。方法：采用0、5、10、20 μg/mL DDP分别处理Ishikawa细胞和顺铂耐药的Ishikawa/DPP细胞，0、5、10、25、50 μmol/L Andro处理Ishikawa/DDP细胞，MTT法检测细胞增殖情况并为后续实验选择合适的给药剂量。将Ishikawa/DDP细胞随机分为对照组、DDP组（DDP干预）、Andro组（DDP、Andro干预）、pcDNA3.1-NC组（转染pcDNA3.1+DDP、Andro干预）、pcDNA3.1-Fas/FasL组（转染pcDNA3.1-Fas/FasL+DDP、Andro干预），24 h后，采用qPCR法检测Fas、FasL mRNA的表达，平板克隆形成实验、Transwell实验和流式细胞术分别检测细胞克隆能力、细胞迁移与侵袭和细胞凋亡，WB法检测增殖细胞核抗原（PCNA）、BAX、Bcl-2、MMP-2、PD-L1、多药耐药蛋白-1（MDR-1）及Fas、FasL蛋白表达。结果：DDP以剂量依赖的方式抑制Ishikawa和Ishikawa/DPP细胞增殖，并且与Ishikawa细胞比较，Ishikawa/DPP细胞对DDP的敏感性更低（均P<0.05）；Andro以剂量依赖性的方式抑制Ishikawa/DPP细胞的增殖（均P<0.05）。Ishikawa/DPP细胞中Fas、FasL的表达水平均高于Ishikawa细胞（均P<0.05）。选取20 μg/mL DDP和25 μmol/L Andro为干预剂量，干预时间24 h。与对照组比较，DDP组Ishikawa/DPP细胞中PD-L1、MDR-1、Fas、FasL mRNA及蛋白表达水平显著升高（P<0.05），而克隆形成率、迁移与侵袭细胞数、凋亡率差异均无统计学意义（均P>0.05）；与DDP组比较，Andro组Ishikawa/DPP细胞中Fas、FasL mRNA表达水平、细胞克隆形成率、迁移与侵袭细胞数、PCNA、Bcl-2、MMP-2、PD-L1、MDR-1、Fas、FasL蛋白表达水平显著降低，BAX蛋白表达水平及凋亡率显著升高（P<0.05或P<0.01），pcDNA3.1-NC组与Andro组类似；与pcDNA3.1-NC组比较，pcDNA3.1-Fas/FasL组Ishikawa/DPP细胞上述指标变化均被逆转（P<0.05）。结论：Andro可能通过抑制Fas/FasL信号轴来抑制Ishikawa/DPP细胞增殖、迁移与侵袭，促进凋亡，从而降低细胞对DDP的耐药性。

Objective To explore the effect of scutellarin on lipopolysaccharide(LPS)induced neuroinflammation in BV-2 microglia cells.Methods BV-2 microglia were cultured and randomly divided into 6 groups:control group(Ctrl),cyclic GMP-AMP synthetase(cGAS)inhibitor RU320521 group(RU.521 group),LPS group,LPS+RU.521 group,LPS+scutellarin pretreatment group(LPS+S)and LPS+S+RU.521 group.The expressions of cGAS,stimulator of interferon gene(STING),nuclear factor kappa B(NF-κB),phosphorylated NF-κB(p-NF-κB),neuroinflammatory factors PYD domains-containing protein 3(NLRP3)and tumor necrosis factor α(TNF-α)in BV-2 microglia were detected by Western blotting and immunofluorescent double staining(n= 3).Results Western blotting and immunofluorescent double staining showed that compared with the control group,the expression of cGAS,STING,p-NF-κB,NLRP3 and TNF-α in BV-2 microglia increased significantly after LPS induction(P<0.05),while the expression of cGAS,STING,p-NF-κB,NLRP3 and TNF-α in LPS+S group were significantly lower than those in LPS group(P<0.05).Treatment with cGAS pathway inhibitor RU.521 showed similar effects as the pre-treatment group with scutellarin.In addition,the change of NF-κB in each group was not statistically significant(P>0.05).Conclusion Scutellarin inhibits the neuroinflammation mediated by BV-2 microglia cells,which may be related to cGAS-STING signaling pathway.

AIM To prepare the soluble microneedles of Aconitum brachypodum Diels alkaloids.METHODS Centrifugal molding method was adopted in the preparation of soluble microneedles.With chondroitin sulfate consumption,PVP K120 consumption and 40%ethanol consumption as influencing factors,piercing rate as an evaluation index,the formulation was optimized by Box-Behnken response surface method,after which the morphology,piercing performance,drug content and in vitro transdermal performance were investigated.RESULTS The optimal formulation was determined to be 123 mg for chondroitin sulfate consumption,298 mg for PVP K120 consumption,and 2.4 mL for 40%ethanol consumption,the piercing rate was 98.3%.The soluble microneedles were yellow and square patch with conoid needle,which could pierce aluminum foil and rat skin,along with the drug content of(0.94±0.025)mg.The soluble microneedle group demonstrated the accumulative permeability rate of 91.4%within 24 h,which was higher than that in the gel ointment group,and the permeability accorded with Higuchi equation.CONCLUSION The soluble microneedles of A.brachypodum alkaloids exhibit good mechanical strength,which can achieve effective transdermal delivery of drugs.

Objective:To investigate the clinical features and outcomes of adolescence-onset methylmalonic acidemia (MMA) and explore preventive strategies.Methods:This was a retrospective case analysis of the phenotypes, genotypes and prognoses of adolescence-onset MMA patients. There were 55 patients diagnosed in Peking University First Hospital from January 2002 to June 2023, the data of symptoms, signs, laboratory results, gene variations, and outcomes was collected. The follow-ups were done through WeChat, telephone, or clinic visits every 3 to 6 months.Results:Among the 55 patients, 31 were males and 24 were females. The age of onset was 12 years old (range 10-18 years old). They visited clinics at Tanner stages 2 to 5 with typical secondary sexual characteristics. Nine cases (16%) were trigged by infection and 5 cases (9%) were triggered by insidious exercises. The period from onset to diagnosis was between 2 months and 6 years. Forty-five cases (82%) had neuropsychiatric symptoms as the main symptoms, followed by cardiovascular symptoms in 12 cases (22%), kidney damage in 7 cases (13%), and eye disease in 12 cases (22%). Fifty-four cases (98%) had the biochemical characteristics of methylmalonic acidemia combined with homocysteinemia, and 1 case (2%) had the isolated methylmalonic acidemia. Genetic diagnosis was obtained in 54 cases, with 20 variants identified in MMACHC gene and 2 in MMUT gene. In 53 children with MMACHC gene mutation,1 case had dual gene variants of PRDX1 and MMACHC, with 105 alleles. The top 5 frequent variants in MMACHC were c.482G>A in 39 alleles (37%), c.609G>A in 17 alleles (16%), c.658_660delAAG in 11 alleles (10%), c.80A>G in 10 alleles (10%), c.567dupT and c.394C>T both are 4 alleles (4%). All patients recovered using cobalamin, L-carnitine, betaine, and symptomatic therapy, and 54 patients (98%) returned to school or work.Conclusions:Patients with adolescence-onset MMA may triggered by fatigue or infection. The diagnosis is often delayed due to non-specific symptoms. Metabolic and genetic tests are crucial for a definite diagnosis. Treatment with cobalamin, L-carnitine, and betaine can effectively reverse the prognosis of MMA in adolescence-onset patients.

Background: Genetic defects in the human thyroid-stimulating hormone (TSH) receptor (TSHR) gene can cause congenital hypothyroidism (CH). However, the biological functions and comprehensive genotype–phenotype relationships for most TSHR variants associated with CH remain unexplored. We aimed to identify TSHR variants in Chinese patients with CH, analyze the functions of the variants, and explore the relationships between TSHR genotypes and clinical phenotypes. Methods: In total, 367 patients with CH were recruited for TSHR variant screening using whole-exome sequencing. The effects of the variants were evaluated by in-silico programs such as SIFT and polyphen2. Furthermore, these variants were transfected into 293T cells to detect their Gs/cyclic AMP and Gq/11 signaling activity. Results: Among the 367 patients with CH, 17 TSHR variants, including three novel variants, were identified in 45 patients, and 18 patients carried biallelic TSHR variants. In vitro experiments showed that 10 variants were associated with Gs/cyclic AMP and Gq/11 signaling pathway impairment to varying degrees. Patients with TSHR biallelic variants had lower serum TSH levels and higher free triiodothyronine and thyroxine levels at diagnosis than those with DUOX2 biallelic variants. Conclusions We found a high frequency of TSHR variants in Chinese patients with CH (12.3%), and 4.9% of cases were caused by TSHR biallelic variants. Ten variants were identified as loss-of-function variants. The data suggest that the clinical phenotype of CH patients caused by TSHR biallelic variants is relatively mild. Our study expands the TSHR variant spectrum and provides further evidence for the elucidation of the genetic etiology of CH.